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Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic demands reliable prognostic models for estimating the risk of long COVID. We developed and validated a prediction model to estimate the probability of known common long COVID symptoms at least 60 days after acute COVID-19. METHODS: The pro...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9668400/ https://www.ncbi.nlm.nih.gov/pubmed/36384641 http://dx.doi.org/10.1186/s41512-022-00135-9 |
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author | Deforth, Manja Gebhard, Caroline E. Bengs, Susan Buehler, Philipp K. Schuepbach, Reto A. Zinkernagel, Annelies S. Brugger, Silvio D. Acevedo, Claudio T. Patriki, Dimitri Wiggli, Benedikt Twerenbold, Raphael Kuster, Gabriela M. Pargger, Hans Schefold, Joerg C. Spinetti, Thibaud Wendel-Garcia, Pedro D. Hofmaenner, Daniel A. Gysi, Bianca Siegemund, Martin Heinze, Georg Regitz-Zagrosek, Vera Gebhard, Catherine Held, Ulrike |
author_facet | Deforth, Manja Gebhard, Caroline E. Bengs, Susan Buehler, Philipp K. Schuepbach, Reto A. Zinkernagel, Annelies S. Brugger, Silvio D. Acevedo, Claudio T. Patriki, Dimitri Wiggli, Benedikt Twerenbold, Raphael Kuster, Gabriela M. Pargger, Hans Schefold, Joerg C. Spinetti, Thibaud Wendel-Garcia, Pedro D. Hofmaenner, Daniel A. Gysi, Bianca Siegemund, Martin Heinze, Georg Regitz-Zagrosek, Vera Gebhard, Catherine Held, Ulrike |
author_sort | Deforth, Manja |
collection | PubMed |
description | BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic demands reliable prognostic models for estimating the risk of long COVID. We developed and validated a prediction model to estimate the probability of known common long COVID symptoms at least 60 days after acute COVID-19. METHODS: The prognostic model was built based on data from a multicentre prospective Swiss cohort study. Included were adult patients diagnosed with COVID-19 between February and December 2020 and treated as outpatients, at ward or intensive/intermediate care unit. Perceived long-term health impairments, including reduced exercise tolerance/reduced resilience, shortness of breath and/or tiredness (REST), were assessed after a follow-up time between 60 and 425 days. The data set was split into a derivation and a geographical validation cohort. Predictors were selected out of twelve candidate predictors based on three methods, namely the augmented backward elimination (ABE) method, the adaptive best-subset selection (ABESS) method and model-based recursive partitioning (MBRP) approach. Model performance was assessed with the scaled Brier score, concordance c statistic and calibration plot. The final prognostic model was determined based on best model performance. RESULTS: In total, 2799 patients were included in the analysis, of which 1588 patients were in the derivation cohort and 1211 patients in the validation cohort. The REST prevalence was similar between the cohorts with 21.6% (n = 343) in the derivation cohort and 22.1% (n = 268) in the validation cohort. The same predictors were selected with the ABE and ABESS approach. The final prognostic model was based on the ABE and ABESS selected predictors. The corresponding scaled Brier score in the validation cohort was 18.74%, model discrimination was 0.78 (95% CI: 0.75 to 0.81), calibration slope was 0.92 (95% CI: 0.78 to 1.06) and calibration intercept was −0.06 (95% CI: −0.22 to 0.09). CONCLUSION: The proposed model was validated to identify COVID-19-infected patients at high risk for REST symptoms. Before implementing the prognostic model in daily clinical practice, the conduct of an impact study is recommended. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41512-022-00135-9. |
format | Online Article Text |
id | pubmed-9668400 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-96684002022-11-18 Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms Deforth, Manja Gebhard, Caroline E. Bengs, Susan Buehler, Philipp K. Schuepbach, Reto A. Zinkernagel, Annelies S. Brugger, Silvio D. Acevedo, Claudio T. Patriki, Dimitri Wiggli, Benedikt Twerenbold, Raphael Kuster, Gabriela M. Pargger, Hans Schefold, Joerg C. Spinetti, Thibaud Wendel-Garcia, Pedro D. Hofmaenner, Daniel A. Gysi, Bianca Siegemund, Martin Heinze, Georg Regitz-Zagrosek, Vera Gebhard, Catherine Held, Ulrike Diagn Progn Res Research BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic demands reliable prognostic models for estimating the risk of long COVID. We developed and validated a prediction model to estimate the probability of known common long COVID symptoms at least 60 days after acute COVID-19. METHODS: The prognostic model was built based on data from a multicentre prospective Swiss cohort study. Included were adult patients diagnosed with COVID-19 between February and December 2020 and treated as outpatients, at ward or intensive/intermediate care unit. Perceived long-term health impairments, including reduced exercise tolerance/reduced resilience, shortness of breath and/or tiredness (REST), were assessed after a follow-up time between 60 and 425 days. The data set was split into a derivation and a geographical validation cohort. Predictors were selected out of twelve candidate predictors based on three methods, namely the augmented backward elimination (ABE) method, the adaptive best-subset selection (ABESS) method and model-based recursive partitioning (MBRP) approach. Model performance was assessed with the scaled Brier score, concordance c statistic and calibration plot. The final prognostic model was determined based on best model performance. RESULTS: In total, 2799 patients were included in the analysis, of which 1588 patients were in the derivation cohort and 1211 patients in the validation cohort. The REST prevalence was similar between the cohorts with 21.6% (n = 343) in the derivation cohort and 22.1% (n = 268) in the validation cohort. The same predictors were selected with the ABE and ABESS approach. The final prognostic model was based on the ABE and ABESS selected predictors. The corresponding scaled Brier score in the validation cohort was 18.74%, model discrimination was 0.78 (95% CI: 0.75 to 0.81), calibration slope was 0.92 (95% CI: 0.78 to 1.06) and calibration intercept was −0.06 (95% CI: −0.22 to 0.09). CONCLUSION: The proposed model was validated to identify COVID-19-infected patients at high risk for REST symptoms. Before implementing the prognostic model in daily clinical practice, the conduct of an impact study is recommended. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41512-022-00135-9. BioMed Central 2022-11-17 /pmc/articles/PMC9668400/ /pubmed/36384641 http://dx.doi.org/10.1186/s41512-022-00135-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Deforth, Manja Gebhard, Caroline E. Bengs, Susan Buehler, Philipp K. Schuepbach, Reto A. Zinkernagel, Annelies S. Brugger, Silvio D. Acevedo, Claudio T. Patriki, Dimitri Wiggli, Benedikt Twerenbold, Raphael Kuster, Gabriela M. Pargger, Hans Schefold, Joerg C. Spinetti, Thibaud Wendel-Garcia, Pedro D. Hofmaenner, Daniel A. Gysi, Bianca Siegemund, Martin Heinze, Georg Regitz-Zagrosek, Vera Gebhard, Catherine Held, Ulrike Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms |
title | Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms |
title_full | Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms |
title_fullStr | Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms |
title_full_unstemmed | Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms |
title_short | Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms |
title_sort | development and validation of a prognostic model for the early identification of covid-19 patients at risk of developing common long covid symptoms |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9668400/ https://www.ncbi.nlm.nih.gov/pubmed/36384641 http://dx.doi.org/10.1186/s41512-022-00135-9 |
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